Defending online reputation systems against collaborative unfair raters through signal modeling and trust
Date of Original Version
Online feedback-based rating systems are gaining popularity. Dealing with collaborative unfair ratings in such systems has been recognized as an important but difficult problem. This problem is challenging especially when the number of honest ratings is relatively small and unfair ratings can contribute to a significant portion of the overall ratings. In addition, the lack of unfair rating data from real human users is another obstacle toward realistic evaluation of defense mechanisms. In this paper, we propose a set of methods that jointly detect smart and collaborative unfair ratings based on signal modeling. Based on the detection, a framework of trust-assisted rating aggregation system is developed. Furthermore, we design and launch a Rating Challenge to collect unfair rating data from real human users. The proposed system is evaluated through simulations as well as experiments using real attack data. Compared with existing schemes, the proposed system can significantly reduce the impact from collaborative unfair ratings. Copyright 2009 ACM.
Proceedings of the ACM Symposium on Applied Computing
Yang, Yafei, Yan Sun, Steven Kay, and Qing Yang. "Defending online reputation systems against collaborative unfair raters through signal modeling and trust." Proceedings of the ACM Symposium on Applied Computing , (2009): 1308-1315. doi:10.1145/1529282.1529575.